Method and apparatus for discriminating cardiac signals in a medical device based on wavelet decomposition analysis

ABSTRACT

A method and device for detecting cardiac signals in a medical device that includes decomposing sensed cardiac signals using a wavelet function to form a corresponding wavelet transform, generating a first wavelet representation corresponding to the wavelet transform that is responsive to RR intervals of the sensed cardiac signals, generating a second wavelet representation that is not responsive to RR intervals associated with the sensed cardiac signals, determining a no lead failure zone in response to the first wavelet representation and the second wavelet representation, and distinguishing the cardiac event from a device failure in response to the determined no lead failure zone. The method and device may also include generating a wavelet representation corresponding to the wavelet transform that is not responsive to RR intervals of the sensed cardiac signals, determining RR intervals associated with the sensed cardiac signals, and determining a no lead failure zone in response to the wavelet representation and the determined RR intervals.

RELATED APPLICATION

The present application claims priority and other benefits from U.S.Provisional Patent Application Ser. No. 60/746,561, filed May 5, 2006,entitled “METHOD AND APPARATUS FOR DISCRIMINATING CARDIAC SIGNALS IN AMEDICAL DEVICE BASED ON WAVELET DECOMPOSITION ANALYSIS”, incorporatedherein by reference in its entirety.

CROSS-REFERENCE TO RELATED APPLICATION

Cross-reference is hereby made to the commonly-assigned related U.S.applications, attorney docket number P25915.01, entitled “METHOD ANDAPPARATUS FOR DISCRIMINATING CARDIAC SIGNALS IN A MEDICAL DEVICE BASEDON WAVELET DECOMPOSITION ANALYSIS”, to Ghanem et al., and attorneydocket number P25915.02, entitled “METHOD AND APPARATUS FORDISCRIMINATING CARDIAC SIGNALS IN A MEDICAL DEVICE BASED ON WAVELETDECOMPOSITION ANALYSIS”, to Ghanem et al., both filed concurrentlyherewith and incorporated herein by reference in their entireties.

FIELD OF THE INVENTION

The present invention relates generally to medical devices, and moreparticularly to a method and apparatus for discriminating cardiacsignals based on wavelet decomposition analysis.

BACKGROUND OF THE INVENTION

Lead failure (set screws, subclavian crush, header, adapter, etc.)remain a major cause of inappropriate detection and therapy in patientsreceiving transvenous implantable cardioverter defibrillator (ICDs).Lead failure accounts for 54% of inappropriate detection due tooversensing. Lead failure typically exhibits as saturated or signalportions with high slew rates.

ICD's detect ventricular arrhythmia whenever a specific number of shortdepolarization intervals is reached. For example, 12 out of 16 intervalsfalling into the fibrillation detection interval (FDI) will trigger VFdetection at which point charging is initiated. Upon charge completion,a shock is delivered. Lead failure due to fast transients in the signal,also exhibit as short depolarization intervals which are ofteninappropriately detected as VF resulting in reduced specificity.

Typically, broken electrodes, lead fractures, or signal saturationsdemonstrate as singularities (fast transients with very large slewrates, step-like transitions) on the recorded electrograms orelectrocardiograms. These are usually closely coupled and short lived.What is needed is a method and apparatus that addresses these signalcharacteristics (sharp fast transitions that are closely coupled intime) during wavelet decomposition analysis in order to detect leadfailure.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects and features of the present invention will be appreciated as thesame becomes better understood by reference to the following detaileddescription of the embodiments of the invention when considered inconnection with the accompanying drawings, wherein:

FIG. 1 is a schematic diagram of an exemplary medical device in whichthe present invention may be utilized;

FIG. 2 is a functional schematic diagram of the device of FIG. 1;

FIG. 3 is a block diagram of discrete wavelet transform decompositionutilized in a method of identifying cardiac signals according to anembodiment of the present invention;

FIG. 4 is a flowchart of a method for detecting lead failure accordingto an embodiment of the present invention;

FIGS. 5-7 are graphical representations of detection of a lead failureaccording to an embodiment of the present invention;

FIG. 8 is a flowchart of a method for detecting lead failure accordingto an embodiment of the present invention; and

FIGS. 9 and 10 are graphical representations of detection of a leadfailure according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

An exemplary implantable cardioverter/defibrillator (ICD) 10 is shown inFIG. 1, with which methods included in the present invention may beused. In accordance with the invention, ICD 10 identifies oversensingand automatically provides a corrective action, e.g., adjusts one ormore detection parameters to avoid future inappropriate detections.Particularly, ICD 10 operates in accordance with originally programmedsensing and detection parameters for a plurality of cardiac cycles, andupon detecting oversensing, automatically provides the corrective actionto avoid possible future inappropriate detections. In this manner, thecorrective actions provided by ICD 10 to avoid future inappropriatedetections are dynamically performed.

The ICD 10 is shown coupled to a heart of a patient by way of threeleads 6, 15, and 16. A connector block 12 receives the proximal end of aright ventricular lead 16, a right atrial lead 15 and a coronary sinuslead 6, used for positioning electrodes for sensing and stimulation inthree or four heart chambers. In FIG. 1, right ventricular lead 16 ispositioned such that its distal end is in the right ventricle forsensing right ventricular cardiac signals and delivering pacing orshocking pulses in the right ventricle. For these purposes, rightventricular lead 16 is equipped with a ring electrode 24, an extendablehelix electrode 26 mounted retractably within an electrode head 28, anda coil electrode 20, each of which are connected to an insulatedconductor within the body of lead 16. The proximal end of the insulatedconductors are coupled to corresponding connectors carried by bifurcatedconnector 14 at the proximal end of lead 16 for providing electricalconnection to the ICD 10.

The right atrial lead 15 is positioned such that its distal end is inthe vicinity of the right atrium and the superior vena cava. Lead 15 isequipped with a ring electrode 21 and an extendable helix electrode 17,mounted retractably within electrode head 19, for sensing and pacing inthe right atrium. Lead 15 is further equipped with a coil electrode 23for delivering high-energy shock therapy. The ring electrode 21, thehelix electrode 17 and the coil electrode 23 are each connected to aninsulated conductor with the body of the right atrial lead 15. Eachinsulated conductor is coupled at its proximal end to a connectorcarried by bifurcated connector 13.

The coronary sinus lead 6 is advanced within the vasculature of the leftside of the heart via the coronary sinus and great cardiac vein. Thecoronary sinus lead 6 is shown in the embodiment of FIG. 1 as having adefibrillation coil electrode 8 that may be used in combination witheither the coil electrode 20 or the coil electrode 23 for deliveringelectrical shocks for cardioversion and defibrillation therapies. Inother embodiments, coronary sinus lead 6 may also be equipped with adistal tip electrode and ring electrode for pacing and sensing functionsin the left chambers of the heart. The coil electrode 8 is coupled to aninsulated conductor within the body of lead 6, which provides connectionto the proximal connector 4.

The electrodes 17 and 21 or 24 and 26 may be used as true bipolar pairs,commonly referred to as a “tip-to-ring” configuration. Further,electrode 17 and coil electrode 20 or electrode 24 and coil electrode 23may be used as integrated bipolar pairs, commonly referred to as a“tip-to-coil” configuration. In accordance with the invention, ICD 10may, for example, adjust the electrode configuration from a tip-to-ringconfiguration, e.g., true bipolar sensing, to a tip-to-coilconfiguration, e.g., integrated bipolar sensing, upon detection ofoversensing in order to reduce the likelihood of future oversensing. Inother words, the electrode polarities can be reselected in response todetection of oversensing in an effort to reduce susceptibility ofoversensing. In some cases, electrodes 17, 21, 24, and 26 may be usedindividually in a unipolar configuration with the device housing 11serving as the indifferent electrode, commonly referred to as the “can”or “case” electrode.

The device housing 11 may also serve as a subcutaneous defibrillationelectrode in combination with one or more of the defibrillation coilelectrodes 8, 20 or 23 for defibrillation of the atria or ventricles. Itis recognized that alternate lead systems may be substituted for thethree lead system illustrated in FIG. 1. While a particularmulti-chamber ICD and lead system is illustrated in FIG. 1,methodologies included in the present invention may adapted for use withany single chamber, dual chamber, or multi-chamber ICD or pacemakersystem, non-transvenous cardiac device, or other cardiac monitoringdevice.

A functional schematic diagram of the ICD 10 is shown in FIG. 2. Thisdiagram should be taken as exemplary of the type of device with whichthe invention may be embodied and not as limiting. The disclosedembodiment shown in FIG. 2 is a microprocessor-controlled device, butthe methods of the present invention may also be practiced with othertypes of devices such as those employing dedicated digital circuitry.

With regard to the electrode system illustrated in FIG. 1, ICD 10 isprovided with a number of connection terminals for achieving electricalconnection to the leads 6, 15, and 16 and their respective electrodes. Aconnection terminal 311 provides electrical connection to the housing 11for use as the indifferent electrode during unipolar stimulation orsensing. The connection terminals 320, 310, and 318 provide electricalconnection to coil electrodes 20, 8 and 23 respectively. Each of theseconnection terminals 311, 320, 310, and 318 are coupled to the highvoltage output circuit 234 to facilitate the delivery of high energyshocking pulses to the heart using one or more of the coil electrodes 8,20, and 23 and optionally the housing 11.

The connection terminals 317 and 321 provide electrical connection tothe helix electrode 17 and the ring electrode 21 positioned in the rightatrium. The connection terminals 317 and 321 are further coupled to anatrial sense amplifier 204 for sensing atrial signals such as P-waves.The connection terminals 326 and 324 provide electrical connection tothe helix electrode 26 and the ring electrode 24 positioned in the rightventricle. The connection terminals 326 and 324 are further coupled to aventricular sense amplifier 200 for sensing ventricular signals.

The atrial sense amplifier 204 and the ventricular sense amplifier 200preferably take the form of automatic gain controlled amplifiers withadjustable sensitivity. Atrial sense amplifier 204 and ventricular senseamplifier 200 receive timing information from pacer timing and controlcircuitry 212. Specifically, atrial sense amplifier 204 and ventricularsense amplifier 200 receive blanking period input, e.g., ABLANK andVBLANK, respectively, which indicates the amount of time the electrodesare “turned off” in order to prevent saturation due to an applied pacingpulse or defibrillation shock.

The general operation of the ventricular sense amplifier 200 and theatrial sense amplifier 204 may correspond to that disclosed in U.S. Pat.No. 5,117,824, by Keimel, et al., incorporated herein by reference inits entirety. Whenever a signal received by atrial sense amplifier 204exceeds an atrial sensitivity, a signal is generated on the P-out signalline 206. Whenever a signal received by the ventricular sense amplifier200 exceeds a ventricular sensitivity, a signal is generated on theR-out signal line 202.

Switch matrix 208 is used to select which of the available electrodesare coupled to a wide band amplifier 210 for use in digital signalanalysis. Selection of the electrodes is controlled by themicroprocessor 224 via data/address bus 218. The selected electrodeconfiguration may be varied as desired for the various sensing, pacing,cardioversion and defibrillation functions of the ICD 10.

Signals from the electrodes selected for coupling to bandpass amplifier210 are provided to multiplexer 220, and thereafter converted tomulti-bit digital signals by A/D converter 222, for storage in randomaccess memory 226 under control of direct memory access circuit 228.Microprocessor 224 may employ digital signal analysis techniques tocharacterize the digitized signals stored in random access memory 226 torecognize and classify the patient's heart rhythm employing any of thenumerous signal processing methodologies known in the art. An exemplarytachyarrhythmia recognition system is described in U.S. Pat. No.5,545,186 issued to Olson et al, incorporated herein by reference in itsentirety.

Upon detection of an arrhythmia, an episode of EGM data, along withsensed intervals and corresponding annotations of sensed events, arepreferably stored in random access memory 226. The EGM signals storedmay be sensed from programmed near-field and/or far-field sensingelectrode pairs. Typically, a near-field sensing electrode pair includesa tip electrode and a ring electrode located in the atrium or theventricle, such as electrodes 17 and 21 or electrodes 26 and 24. Afar-field sensing electrode pair includes electrodes spaced furtherapart such as any of: the defibrillation coil electrodes 8, 20 or 23with housing 11; a tip electrode 17 or 26 with housing 11; a tipelectrode 17 or 26 with a defibrillation coil electrode 20 or 23; oratrial tip electrode 17 with ventricular ring electrode 24. The use ofnear-field and far-field EGM sensing of arrhythmia episodes is describedin U.S. Pat. No. 5,193,535, issued to Bardy, incorporated herein byreference in its entirety. Annotation of sensed events, which may bedisplayed and stored with EGM data, is described in U.S. Pat. No.4,374,382 issued to Markowitz, incorporated herein by reference in itsentirety.

The telemetry circuit 330 receives downlink telemetry from and sendsuplink telemetry to an external programmer, as is conventional inimplantable anti-arrhythmia devices, by means of an antenna 332. Data tobe uplinked to the programmer and control signals for the telemetrycircuit are provided by microprocessor 224 via address/data bus 218. EGMdata that has been stored upon arrhythmia detection or as triggered byother monitoring algorithms may be uplinked to an external programmerusing telemetry circuit 330. Received telemetry is provided tomicroprocessor 224 via multiplexer 220. Numerous types of telemetrysystems known in the art for use in implantable devices may be used.

The remainder of the circuitry illustrated in FIG. 2 is an exemplaryembodiment of circuitry dedicated to providing cardiac pacing,cardioversion and defibrillation therapies. The pacer timing and controlcircuitry 212 includes programmable digital counters which control thebasic time intervals associated with various single, dual ormulti-chamber pacing modes or anti-tachycardia pacing therapiesdelivered in the atria or ventricles. Pacer circuitry 212 alsodetermines the amplitude of the cardiac pacing pulses under the controlof microprocessor 224.

During pacing, escape interval counters within pacer timing and controlcircuitry 212 are reset upon sensing of R-waves or P-waves as indicatedby signals on lines 202 and 206, respectively. In accordance with theselected mode of pacing, pacing pulses are generated by atrial paceroutput circuit 214 and ventricular pacer output circuit 216. The paceroutput circuits 214 and 216 are coupled to the desired electrodes forpacing via switch matrix 208. The escape interval counters are resetupon generation of pacing pulses, and thereby control the basic timingof cardiac pacing functions, including anti-tachycardia pacing.

The durations of the escape intervals are determined by microprocessor224 via data/address bus 218. The value of the count present in theescape interval counters when reset by sensed R-waves or P-waves can beused to measure R-R intervals and P-P intervals for detecting theoccurrence of a variety of arrhythmias.

The microprocessor 224 includes associated read-only memory (ROM) inwhich stored programs controlling the operation of the microprocessor224 reside. A portion of the random access memory (RAM) 226 may beconfigured as a number of recirculating buffers capable of holding aseries of measured intervals for analysis by the microprocessor 224 forpredicting or diagnosing an arrhythmia.

In response to the detection of tachycardia, anti-tachycardia pacingtherapy can be delivered by loading a regimen from microprocessor 224into the pacer timing and control circuitry 212 according to the type oftachycardia detected. In the event that higher voltage cardioversion ordefibrillation pulses are required, microprocessor 224 activates thecardioversion and defibrillation control circuitry 230 to initiatecharging of the high voltage capacitors 246 and 248 via charging circuit236 under the control of high voltage charging control line 240. Thevoltage on the high voltage capacitors is monitored via a voltagecapacitor (VCAP) line 244, which is passed through the multiplexer 220.When the voltage reaches a predetermined value set by microprocessor224, a logic signal is generated on the capacitor full (CF) line 254,terminating charging. The defibrillation or cardioversion pulse isdelivered to the heart under the control of the pacer timing and controlcircuitry 212 by an output circuit 234 via a control bus 238. The outputcircuit 234 determines the electrodes used for delivering thecardioversion or defibrillation pulse and the pulse wave shape.

In one embodiment, the ICD 10 may be equipped with a patientnotification system 150. Any patient notification method known in theart may be used such as generating perceivable twitch stimulation or anaudible sound. A patient notification system may include an audiotransducer that emits audible sounds including voiced statements ormusical tones stored in analog memory and correlated to a programming orinterrogation operating algorithm or to a warning trigger event asgenerally described in U.S. Pat. No. 6,067,473 issued to Greeninger etal., incorporated herein by reference in its entirety.

Wavelet decomposition analysis offers the unique opportunity to analyzelocalized time and frequency information content in the intracardiacelectrogram. Using dyadic wavelet decomposition, it is possible tocharacterize a signal from the wavelet transform maxima. Additionally,using wavelets with increasing number of vanishing moments, it ispossible to characterize the smoothness of the input signal. The localextrema in the wavelet transform correlate with the signal transientsand its derivatives. The present invention relates to evaluation of thewavelet transform computed using one or more mother wavelets, such as aHaar wavelet or a Daubechies 4^(th) order wavelet, for example, for thedevelopment of potential discriminators that can differentiate leadfailure (or any sharp signal transients or singularities) fromventricular fibrillation (VF) by wavelet decomposition analysis.

Wavelet decomposition involves representing a given signal as a weightedsuperposition of linear combinations of some basis wavelets (such asHaar or Daubechies wavelets) that are dilated and scaled. The weightscorresponding to these bases are determined from the inner productbetween the given signal and the particular scaled and dilated wavelet.The basis wavelets are functions that have a zero mean, are typically offinite support (duration) and satisfy a specific condition. The waveletsare scaled and dilated to evaluate different time and frequency contentinformation in the signal. A short duration wavelet has good timeresolution but poor frequency resolution. A long duration wavelet haspoor time resolution but good frequency resolution. Using waveletdecomposition, both time and frequency content can be analyzed atdifferent scales.

FIG. 3 is a block diagram of discrete wavelet transform decompositionutilized in a method of identifying cardiac signals according to anembodiment of the present invention. As illustrated in FIG. 3, at eachlevel (scale s), the signal is decomposed into a pair of approximation(lowpass frequency content) and detail (highpass frequency content)coefficients.

On a subsequent level the approximation coefficients are furtherdecomposed into approximation and detail coefficients. This process isperformed up to 2^(N)th level. In the example of FIG. 3, thedecomposition is performed up to 4 levels implying a scale of 2⁴ as thecoarse scale. In particular, the signal f(x) is decomposed at the firstlevel, S=2, into a first pair that includes approximation coefficients360 and detail coefficients 362. At the next or second level, S=2², theapproximation coefficients 360 from the previous level are furtherdecomposed into a second pair that includes approximation coefficients364 and detail coefficients 366. At the next or third level, S=2³, theapproximation coefficients 364 from the previous level are furtherdecomposed into a second pair that includes approximation coefficients368 and detail coefficients 370. At the next or fourth level, S=2⁴, theapproximation coefficients 368 from the previous level are furtherdecomposed into a second pair that includes approximation coefficients372 and detail coefficients 374. In this way, the input signal f(x) istherefore completely described by approximation coefficients 372,approximation coefficients 368, approximation coefficients 364,approximation coefficients 360, detail coefficients 374, detailcoefficients 370, detail coefficients 366 and detail coefficients 362 bysummation of the sequences generated from the inner product of each setof these coefficients with the input signal f(x).

By studying the detail and approximation coefficients it is possible toanalyze signal characteristics during signal singularities/transients/orsharp transitions. The present invention utilizes the approximatecoefficients at the coarse scale (approximation coefficients 372) toprovide an estimate of the variation of transients in the signal and thedetail coefficients at the finest scale (detail coefficients 362) toprovide a rate estimate.

By applying the Haar wavelet and computing the detail (highpass) andapproximation (lowpass) representations (herein referred to as detailsequences and approximation sequences), statistical measures are thencomputed in order to discriminate signal singularities from VF. At finescales, the detail sequences represent the ventricular depolarizations(R-waves). At coarse scales, the wavelet transform approximationsrepresent the DC shift or average value of the signal.

Current known lead failure algorithms have incorporated lead impedancemeasurements (as a surrogate to lead quality: high values signify opencircuit or broken lead) and number of short RR interval counts (RR isdefined as the time interval between consecutive depolarization, i.e.R-waves). However, it is not possible to measure electrode impedancewhen pacing circuitry is not available. More recent algorithms rely onmeasurements made on the far field electrogram when the sensed rate fromthe near field electrogram falls in the VTNF shock zone. In the presentinvention, a potential approach for lead failure detection utilizes thenear field electrogram without relying on far field electrogrammeasurements. The potential for this approach lies in the possibility todetect electrode failure when impedance measurements through theelectrode to assess tissue/electrode/lead functionality are notpossible. In addition, this approach could be used to detect suddentransients in the ECG or electrogram and therefore preclude the need forblanking post pace or post shock. Today's ICDs blank the sensingamplifier hardware post pacing or post shock in order not to sense therecovery from polarization which often exhibits as sharp and fasttransients. Using the technique presented by the present invention,polarization, like singularities, can be detected and detection can bewithheld accordingly without the need to blank the sensing amplifiers.

In order to detect short durations of signal discontinuities, it isessential to choose a wavelet function such that the length of thelowpass and highpass wavelet decomposition filters is short. Thesefilters are utilized to compute the approximation sequences and thedetail sequences. The use of the wavelet transform in accordance withthe invention adds value in representing a signal that is a mix of sharptransients and slowly varying components as is the case with leadfailure, saturated signals, or signals recovering post shock due toelectrode polarization. According to the present invention, it isfeasible to use wavelet decomposition to characterize lead failure (ormore generally singularities in the signal) and to potentiallydiscriminate that from ventricular fibrillation.

In a method of identifying cardiac signals according to an embodiment ofthe present invention, a cardiac signal is decomposed using a firstwavelet function at a first plurality of scales to form a correspondingwavelet transform. First approximation and detail sequences aredetermined in response to the first plurality of scales.

In another embodiment of the present invention, a cardiac signal isdecomposed using a first wavelet function at a first plurality of scalesto form a corresponding wavelet transform, and first approximationsequences are determined in response to the first plurality of scales. Acomparison of dispersion associated with the determined firstapproximation sequences is then made.

According to another embodiment of the present invention, a waveletrepresentation of the wavelet transform is reconstructed usingpredetermined approximation coefficients of the determined firstapproximation coefficients, wherein the comparing is in response to thereconstructed wavelet representation. In another embodiment, the cardiacsignal is decomposed using a second wavelet function at a secondplurality of scales to form a corresponding second wavelet transform,second approximation sequences are determined in response to the secondplurality of scales, and dispersion associated with the determinedsecond approximation sequences is compared.

According to an embodiment of the present invention, a waveletrepresentation of the second wavelet transform is constructed usingpredetermined approximation coefficients of the determined secondapproximation coefficients, wherein the comparing dispersion associatedwith the determined second approximation coefficients is in response tothe reconstructed wavelet representation of the second wavelettransform. The compared dispersion associated with the reconstructedwavelet representation of the first wavelet transform and thereconstructed wavelet representation of the second wavelet transform isthen analyzed.

According to the present invention the cardiac signal is identified asbeing associated with ventricular fibrillation in response to thecompared dispersion being less than a dispersion threshold, and thecardiac signal is identified as being associated with a corruption of alead in response to the compared dispersion not being less than thedispersion threshold.

It is understood that, according to the present invention, the firstapproximation coefficients may be either the same or different than thesecond approximation coefficients.

FIG. 4 is a flowchart of a method for detecting lead failure accordingto an embodiment of the present invention. As illustrated in FIG. 4,according to an embodiment of the invention, an EGM signal associatedwith cardiac signals sensed by the device is acquired, Block 400, sothat a wavelet decomposition is performed, Block 402, on the obtainedsignal using a wavelet transform, Block 404, such as a Haar wavelet forexample. According to an embodiment of the invention, the waveletdecomposition utilized in Block 402 is performed to 4 levels, implying ascale of 2⁴ as the course scale. Once the wavelet decomposition has beenperformed, the device selects the approximation coefficients, Block 406,previously obtained from the determined wavelet decomposition of Block402. For example, according to an embodiment of the present invention,the device selects the approximation coefficients at the most coursescale, i.e., S=2⁴, as the signal variation characteristic.

The approximation sequence, or signal is then re-constructed, Block 408,using the initially detected EGM signal, Block 400, obtained prior tothe wavelet decomposition, Block 402, and the selected approximationcoefficients, Block 406. Once the approximation sequence has beenreconstructed in Block 408, the device determines a signal variationmetric, Block 410, described below, based on the reconstructedapproximation sequence, Block 408. In addition to determining a signalvariation metric, Block 410, the device also determines a signal ratemetric, Block 412, based on the initially detected EGM signal obtainedin Block 400. The device then determines whether both the signalvariation metric and the signal rate metric are within a no lead failurezone, Block 414, described below. If both the signal variation metricand the signal rate metric are within the no lead failure zone, Yes inBlock 414, a lead failure is not detected, Block 416, and the detectionand therapy delivery process resumes as normal. If one or both of thesignal variation metric and the signal rate metric are outside the nolead failure zone, No in Block 414, a lead failure is detected, Block418, and VTNF detection is withheld, thereby enhancing specificity.Additionally, repeated lead failure detections may trigger a patientalert.

According to the present invention, the signal variation metric and thesignal rate metric are chosen in such a way as to maximize both thesensitivity and the specificity of the device in identifying a leadfailure. For example, the inventors have discovered that bothspecificity and sensitivity are maximized by using the combination of amean rectified amplitude MRA associated with the reconstructed signal ofBlock 408 as the signal variation metric, and a mean absolute differenceMAD_(rr) between RR intervals associated with the initially detected EGMsignal obtained in Block 400 as the signal rate metric.

Therefore, during the determination of the signal variation metric ofBlock 410, the device generates sample amplitudes of the reconstructedsignal of Block 408 over a predetermined window, such as a three secondwindow for example, so that if the device generates the amplitudesamples at a rate of 256 samples per second, the sampling results in 768total amplitude samples being generated. The device then determines themean rectified amplitude MRA associated with the three second window bydividing the sum of the absolute values of each of the amplitude samplesby the total number of sample, and sets the signal variation metric,Block 410, equal to the determined mean rectified amplitude. Similarly,in order to determine the signal rate metric of Block 412, the devicefirst determines an overall mean of a predetermined number of RRintervals from the initially detected EGM signal, Block 400, determinesthe mean absolute difference MAD_(rr) between the RR intervals bydividing the sum of the absolute difference between each RR interval andthe predetermined overall mean by the total number of samples, and setsthe signal rate metric equal to the determined mean absolute differenceMAD_(rr).

According to another embodiment of the invention, the signal rate metricis determined based on the determined mean absolute difference MAD_(rr),as described above. However, rather than utilizing the mean rectifiedamplitude MRA for the signal variation metric, the device determines anamplitude dispersion DISP_(A) of the sample amplitudes for the threesecond window of the reconstructed approximation sequence by determininga difference between a maximum amplitude of the sample amplitudes and aminimum amplitude of the samples amplitudes, and sets the signalvariation metric equal to the determined amplitude dispersion DISP_(A).Therefore, both specificity and sensitivity are maximized by using thecombination of an amplitude dispersion DISP_(A) associated with thereconstructed signal of Block 408 as the signal variation metric, and amean absolute difference MAD_(rr) between RR intervals associated withthe initially detected EGM signal obtained in Block 400 as the signalrate metric.

FIGS. 5-7 are graphical representations of detection of a lead failureaccording to an embodiment of the present invention. FIGS. 5 and 6 areexemplary illustrations of an appropriately detected lead failure and VFevent. As illustrated in FIGS. 5 and 6, it can be deduced that the meanabsolute difference between RR-intervals 501 associated with adjacentv-senses 500 of an initially detected EGM signal 502 during a leadfailure is greater than the mean absolute difference betweenRR-intervals 601 associated with adjacent v-senses 600 of an initiallydetected EGM signal 602 when there is no lead failure. In addition, botha mean rectified amplitude associated with an approximation sequence atthe most course scale 504 during lead failure is greater compared to themean rectified amplitude of an approximation sequence at the most coursescale 604 when there is VF with no lead failure, and the amplitudedispersion associated with an approximation sequence at the most coursescale 504 during lead failure is greater compared to the amplitudedispersion associated with an approximation sequence at the most coursescale 604 when there is VF with no lead failure.

Therefore, as illustrated in FIG. 7, according to an embodiment of thepresent invention, a no lead failure zone 620 is defined based on therelationship between a predetermined mean rectified amplitude MRAthreshold associated with the reconstructed signal of Block 408 and amean absolute difference MAD_(rr) threshold associated with RR intervalsof the initially detected EGM signal. For example, the no lead failurezone 620 is defined by a first boundary 622 associated with the MRAthreshold and a second boundary 624 associated with the MAD_(rr)threshold. According to an embodiment of the present invention,illustrated in FIG. 7 for example, the MRA amplitude threshold is set as4.7 and the MAD_(rr) threshold is set as 52.5 ms. According to anotherembodiment, a no lead failure zone 620 is defined based on therelationship between a predetermined amplitude dispersion DISP_(A)threshold associated with the reconstructed signal of Block 408 and amean absolute difference MAD_(rr) threshold associated with RR intervalsof the initially detected EGM signal. In particular, for example, the nolead failure zone 620 is defined by a first boundary 622 associated withthe amplitude dispersion DISP_(A) threshold and a second boundary 624associated with the mean absolute difference MAD_(rr) threshold.According to an embodiment of the present invention, the dispersionDISP_(A) threshold is set as 7 and the MAD_(rr) threshold is set as 70ms.

In this way, the signal variation metric and the signal rate metric aredetermined to be within the no lead failure zone 620 and therefore leadfailure is not detected if both the mean rectified amplitude is lessthan the first boundary 622, i.e., the MRA threshold in one embodimentor the dispersion DISP_(A) threshold in another embodiment, and the meanof the absolute differences is less than the second boundary, i.e., theMAD_(rr) threshold.

FIG. 8 is a flowchart of a method for detecting lead failure accordingto an embodiment of the present invention. As illustrated in FIG. 8,according to another embodiment of the invention, an EGM signalassociated with cardiac signals sensed by the device is acquired, Block700, so that a wavelet decomposition is performed, Block 702, on theobtained signal using a wavelet transform, Block 704, such as a Haarwavelet for example. According to an embodiment of the invention, thewavelet decomposition utilized in Block 702 is performed to 4 levels,implying a scale of 2⁴ as the course scale. Once the waveletdecomposition has been performed, the device selects the approximationcoefficients, Block 706, previously obtained from the determined waveletdecomposition of Block 702. For example, according to an embodiment ofthe present invention, the device selects the approximation coefficientat the most course scale, i.e., S=2⁴.

The approximation sequence, or signal is then re-constructed, Block 708,using the initially detected EGM signal, Block 700, obtained prior tothe wavelet decomposition, Block 702, and the selected approximationcoefficient, Block 706. Once the approximation sequence has beenreconstructed in Block 708, the device determines a signal variationmetric, Block 710, described below, based on the reconstructed signal,Block 708.

Once the wavelet decomposition has been performed, Block 702, the devicealso selects detail coefficients, Block 707, obtained previously fromthe determined wavelet decomposition of Block 706. For example,according to an embodiment of the invention, the device selects thedetail coefficients at the finest scale, i.e., S=2, and the detailcoefficient of the next finest scale, i.e., S=2².

The detail sequence, or signal, is then reconstructed, Block 709, usingthe initially detected EGM signal, Block 700, obtained prior to thewavelet decomposition, Block 702, and the selected detail coefficients,Block 707. For example, the device sets the reconstructed detailsequence, Block 709, as the sum of the sequences reconstructed from eachof the two detail coefficients at the finest scale (S=2) and the nextfinest scale (S=2²). Once the detail sequence has been reconstructed inBlock 709, the device determines a signal rate metric, Block 712,described below, based on the reconstructed detail sequence, Block 709.

Once both the signal variation metric and the signal rate metric aredetermined, the device determines whether both the signal variationmetric and the signal rate metric are within a no lead failure zone,Block 714, described below. If both the signal variation metric and thesignal rate metric are within the no lead failure zone, Yes in Block714, a lead failure is not detected, Block 716, and the detection andtherapy delivery process resumes as normal. If one or both of the signalvariation metric and the signal rate metric are outside the no leadfailure zone, No in Block 714, a lead failure is detected, Block 718,and VTNF detection is withheld, thereby enhancing specificity. Inaddition, repeated lead failure detections may trigger a patient alert.

As described above, the signal variation metric and the signal ratemetric are chosen in such a way as to maximize both the sensitivity andthe specificity of the device in identifying a lead failure. Forexample, the inventors have discovered that both specificity andsensitivity are maximized by using the combination of a mean rectifiedamplitude MRA associated with the reconstructed signal of Block 708 asthe signal variation metric in Block 710, and a mean absolute differenceMAD_(rr) between RR intervals associated with the reconstructed detailsequence obtained in Block 709 as the signal rate metric of Block 712.

Therefore, during the determination of the signal variation metric ofBlock 710, the device determines the generated sample amplitudes of thereconstructed signal of Block 708 over a predetermined window, such as athree second window for example, so that if the device generates theamplitude samples at a rate of 256 samples per second, the samplingresults in 768 total amplitude samples being generated. The device thendetermines the mean rectified amplitude MRA associated with the threesecond window by dividing the sum of the absolute values of each of theamplitude samples by the number of samples, and sets the signalvariation metric, Block 710, equal to the determined mean rectifiedamplitude MRA. Similarly, in order to determine the signal rate metricof Block 712, the device first determines an overall mean of apredetermined number of RR intervals from the reconstructed detailsequence, Block 709, determines the mean absolute difference MAD_(rr)between the RR intervals by dividing the sum of the absolute differencesbetween each RR interval and the overall mean by the number of samples,and sets the signal rate metric equal to the determined mean absolutedifference MAD_(rr).

According to another embodiment of the invention, the signal ratecharacteristic is determined based on the determined mean absolutedifference MAD_(rr), as described above in reference to FIG. 8. However,rather than utilizing the mean rectified amplitude MRA for the signalvariation metric, the device determines an amplitude dispersion DISP_(A)of the sample amplitudes for the three second window of thereconstructed approximation sequence by determining a difference betweena maximum amplitude of the sample amplitudes and a minimum amplitude ofthe samples amplitudes, and sets the signal variation metric equal tothe determined amplitude dispersion DISP_(A). Therefore, bothspecificity and sensitivity are maximized by using the combination of anamplitude dispersion DISP_(A) associated with the reconstructed signalof Block 408 as the signal variation metric, and a mean absolutedifference MAD_(rr) between RR intervals associated with thereconstructed detail sequence obtained in Block 709 as the signal ratemetric.

FIGS. 9 and 10 are graphical representations of detection of a leadfailure according to an embodiment of the present invention. FIGS. 9 and10 are exemplary illustrations of an appropriately detected lead failureand VF event. As illustrated in FIGS. 9 and 10, it can be deduced thatthe mean absolute difference between RR-intervals 703 associated withadjacent signal waves (i.e., R-waves) 701 of a detail sequence 705associated with the sum of the detail sequence at the finest scale, S=2,and the detail sequence of the next finest scale, S=2² to generate thereconstructed detail sequence from Block 709 during a lead failure isgreater than the mean absolute difference between RR-intervals 721associated with adjacent R-waves 723 of a detail sequence 725 associatedwith the sum of the detail sequence at the finest scale, S=2, and thedetail sequence of the next finest scale, S=2² to generate thereconstructed detail sequence from Block 709 resulting when there is VFwith no lead failure. In addition, both a mean rectified amplitudeassociated with an approximation sequence at the most course scale 727during lead failure is greater compared to an approximation sequence atthe most course scale 729 when there is VF with no lead failure, and theamplitude dispersion associated with an approximation sequence at themost course scale 727 during lead failure is greater compared to theamplitude dispersion associated with an approximation sequence at themost course scale 729 when there is no lead failure.

Therefore, according to an embodiment of the invention of FIG. 8, the nolead failure zone 620 of FIG. 7 is defined based on the relationshipbetween a predetermined mean rectified amplitude MRA thresholdassociated with the reconstructed approximation sequence of Block 708and a mean absolute difference MAD_(rr) threshold associated with thereconstructed detail sequence of Block 709 that is derived from one ormore detail coefficients. According to the embodiment of FIG. 8, similarto the embodiment of FIG. 4, the MRA amplitude threshold is set as 4.7and the MAD_(rr) threshold is set as 52.5 ms. Similarly, according toanother embodiment, the no lead failure zone 620 is defined based on therelationship between a predetermined amplitude dispersion DISP_(A)threshold associated with the reconstructed signal of Block 408 and amean absolute difference MAD_(rr) threshold associated with thereconstructed detail sequence of Block 709 that is derived from one ormore detail coefficients. In particular, for example, the no leadfailure zone 620 is defined by a first boundary 622 associated with theamplitude dispersion DISP_(A) threshold and a second boundary 624associated with the mean absolute difference MAD_(rr) threshold.According to an embodiment of the present invention, the dispersionDISP_(A) threshold is set as 7 and the MAD_(rr) threshold is set as 70ms.

In this way, the signal variation metric and the signal rate metric aredetermined to be within the no lead failure zone 620 and therefore leadfailure is not detected if both the mean rectified amplitude is lessthan the first boundary 622, i.e., the MRA threshold in one embodimentor the dispersion DISP_(A) threshold in another embodiment, and the meanof the absolute differences is less than the second boundary, i.e., theMAD_(rr) threshold.

As can be seen from the exemplary embodiments of FIGS. 4 and 8, theinvention determines a lead failure using either a signal variationmetric determined from a reconstructed approximation sequence resultingfrom wavelet decomposition combined with a mean absolute differenceassociated with RR intervals obtained either from the EGM signal or fromthe reconstructed detail sequence resulting from the waveletdecomposition. However, other discriminators may be utilized todetermine lead failure according to the present invention. For example,according to another embodiment of the invention, the determination oflead failure is made using a metric based solely on one of a meanrectified amplitude, a mean amplitude difference, and an amplitudedispersion associated with the reconstructed approximation sequence ofthe wavelet decomposition, using the methods described above. Asexemplary thresholds, the metric is determined to be in the no leadfailure zone and therefore no lead failure is determined, if the meanrectified amplitude is less than 0.9, if the mean amplitude differenceis less than 1, and if the amplitude dispersion is less than 5.9.

In addition, according to another embodiment of the invention, thedetermination of lead failure is made using a metric based solely on oneof a mean interval difference and an interval dispersion associated withRR intervals from either the EGM signal or RR intervals from areconstructed detail sequence of the wavelet decomposition. As exemplarythresholds, the metric is determined to be in the no lead failure zoneand therefore no lead failure is determined, if the mean amplitudedifference is less than 54.5 ms, and if the amplitude dispersion is lessthan 249.9 ms.

In current known current implantable cardiac devices, detection of acardiac event is performed by keeping track of the number of RRintervals falling within a fibrillation detect interval (FDI) or tachydetect interval (TDI). According to an embodiment of the presentinvention, as RR intervals are acquired and whenever the NID is reached(e.g. 12/16 or 18/24 within the TDI or FDI), then the prior 3-secintracardiac EGM leading up to the time when the NID is met is processedaccording to the current embodiment to determine if there's leadfailure. If lead failure is detected then VTNF detection is withheld,otherwise detection process resumes as is currently defined.

While a particular embodiment of the present invention has been shownand described, modifications may be made. It is therefore intended inthe appended claims to cover all such changes and modifications, whichfall within the true spirit and scope of the invention.

1. A method of detecting cardiac signals in a medical device,comprising: sensing cardiac signals to identify a predetermined cardiacevent; decomposing the sensed cardiac signals using a wavelet functionto form a corresponding wavelet transform; generating a first waveletrepresentation corresponding to the wavelet transform, the first waveletrepresentation being responsive to RR intervals of the sensed cardiacsignals; generating a second wavelet representation corresponding to thewavelet transform, the second wavelet representation not beingresponsive to RR intervals associated with the sensed cardiac signals;determining a no lead failure zone in response to the first waveletrepresentation and the second wavelet representation; and distinguishingthe cardiac event from a device failure in response to the determined nolead failure zone.
 2. The method of claim 1, wherein the wavelettransform is a Haar wavelet transform, the first wavelet representationcorresponds to a detail sequence, and the second wavelet representationcorresponds to an approximation sequence.
 3. The method of claim 1,wherein the wavelet transform is a Haar wavelet transform and the sensedcardiac signals are decomposed at a plurality of scales, the methodfurther comprising: determining approximation coefficients and detailcoefficients in response to the plurality of scales; reconstructing thesecond wavelet representation using predetermined approximationcoefficients of the determined approximation coefficients; andreconstructing the first wavelet representation using predetermineddetail coefficients of the determined detail coefficients, wherein theno lead failure zone is defined by a first boundary associated with thereconstructed first wavelet representation and a second boundaryassociated with the reconstructed second wavelet representation.
 4. Themethod of claim 3, wherein the first boundary is determined in responseto one of a mean absolute difference and a dispersion of RR intervals ofthe reconstructed first wavelet representation and the second boundaryis determined in response to one of a mean rectified amplitude, a meanabsolute difference, and a dispersion of the reconstructed secondwavelet representation.
 5. The method of claim 3, wherein thepredetermined approximation coefficients correspond to a coarsest scaleof the plurality of scales and the detail coefficients correspond to afinest scale of the plurality of scales.
 6. The method of claim 5,wherein the approximation coefficients are determined at a fourth scaleof the plurality of scales and the detail coefficients are determined ata first scale of the plurality of scales.
 7. The method of claim 3,wherein the detail coefficients are determined at a first scale of theplurality of scales to form a first detail sequence and a second scaleof the plurality of scales to form a second detail sequence, and whereinthe first wavelet representation is reconstructed in response to a sumof the first detail sequence and the second detail sequence.
 8. A methodof detecting cardiac signals in a medical device, comprising: sensingcardiac signals; decomposing the sensed cardiac signals using a waveletfunction to form a corresponding wavelet transform; generating a waveletrepresentation corresponding to the wavelet transform, the waveletrepresentation not being responsive to RR intervals of the sensedcardiac signals; determining RR intervals associated with the sensedcardiac signals; determining a no lead failure zone in response to thewavelet representation and the determined RR intervals; anddistinguishing the cardiac event from a device failure in response tothe determined no lead failure zone.
 9. The method of claim 8, whereinthe wavelet transform is a Haar wavelet transform and the waveletrepresentation corresponds to an approximation sequence.
 10. The methodof claim 8, wherein the wavelet transform is a Haar wavelet transformand the sensed cardiac signals are decomposed at a plurality of scales,the method further comprising: determining approximation coefficients inresponse to the plurality of scales; and reconstructing the waveletrepresentation using predetermined approximation coefficients of thedetermined approximation coefficients, wherein the no lead failure zoneis defined by a first boundary associated with the reconstructed waveletrepresentation and a second boundary associated with the determined RRintervals.
 11. The method of claim 10, wherein the first boundary isdetermined in response to one of a mean rectified amplitude, a meanabsolute difference, and a dispersion of the reconstructed waveletrepresentation and the second boundary is determined in response to oneof a mean absolute difference and a dispersion of the determined RRintervals.
 12. The method of claim 10, wherein the predeterminedapproximation coefficients correspond to a coarsest scale of theplurality of scales.
 13. A medical device, comprising: means for sensingcardiac signals to identify a predetermined cardiac event; means fordecomposing the sensed cardiac signals using a wavelet function to forma corresponding wavelet transform; means for generating a first waveletrepresentation corresponding to the wavelet transform, the first waveletrepresentation being responsive to RR intervals of the sensed cardiacsignals; means for generating a second wavelet representationcorresponding to the wavelet transform, the second waveletrepresentation not being responsive to RR intervals associated with thesensed cardiac signals; means for determining a no lead failure zone inresponse to the first wavelet representation and the second waveletrepresentation; and means for distinguishing the cardiac event from adevice failure in response to the determined no lead failure zone. 14.The device of claim 13, wherein the wavelet transform is a Haar wavelettransform, the first wavelet representation corresponds to a detailsequence, and the second wavelet representation corresponds to anapproximation sequence.
 15. The device of claim 13, wherein the wavelettransform is a Haar wavelet transform and the sensed cardiac signals aredecomposed at a plurality of scales, the device further comprising:means for determining approximation coefficients and detail coefficientsin response to the plurality of scales; means for reconstructing thesecond wavelet representation using predetermined approximationcoefficients of the determined approximation coefficients; and means forreconstructing the first wavelet representation using predetermineddetail coefficients of the determined detail coefficients, wherein theno lead failure zone is defined by a first boundary associated with thereconstructed first wavelet representation and a second boundaryassociated with the reconstructed second wavelet representation.
 16. Thedevice of claim 15, wherein the first boundary is determined in responseto one of a mean absolute difference and a dispersion of RR intervals ofthe reconstructed first wavelet representation and the second boundaryis determined in response to one of a mean rectified amplitude, a meanabsolute difference, and a dispersion of the reconstructed secondwavelet representation.
 17. The device of claim 15, wherein thepredetermined approximation coefficients correspond to a coarsest scaleof the plurality of scales and the detail coefficients correspond to afinest scale of the plurality of scales.
 18. The device of claim 17,wherein the approximation coefficients are determined at a fourth scaleof the plurality of scales and the detail coefficients are determined ata first scale of the plurality of scales.
 19. The device of claim 15,wherein the detail coefficients are determined at a first scale of theplurality of scales to form a first detail sequence and a second scaleof the plurality of scales to form a second detail sequence, and whereinthe first wavelet representation is reconstructed in response to a sumof the first detail sequence and the second detail sequence.
 20. Amedical device, comprising: means for sensing cardiac signals; means fordecomposing the sensed cardiac signals using a wavelet function to forma corresponding wavelet transform; means for generating a waveletrepresentation corresponding to the wavelet transform, the waveletrepresentation not being responsive to RR intervals of the sensedcardiac signals; means for determining RR intervals associated with thesensed cardiac signals; means for determining a no lead failure zone inresponse to the wavelet representation and the determined RR intervals;and means for distinguishing the cardiac event from a device failure inresponse to the determined no lead failure zone.
 21. The device of claim20, wherein the wavelet transform is a Haar wavelet transform and thewavelet representation corresponds to an approximation sequence.
 22. Thedevice of claim 11, wherein the wavelet transform is a Haar wavelettransform and the sensed cardiac signals are decomposed at a pluralityof scales, the device further comprising: means for determiningapproximation coefficients in response to the plurality of scales; andmeans for reconstructing the wavelet representation using predeterminedapproximation coefficients of the determined approximation coefficients,wherein the no lead failure zone is defined by a first boundaryassociated with the reconstructed wavelet representation and a secondboundary associated with the determined RR intervals.
 23. The device ofclaim 22, wherein the first boundary is determined in response to one ofa mean rectified amplitude, a mean absolute difference, and a dispersionof the reconstructed wavelet representation and the second boundary isdetermined in response to one of a mean absolute difference and adispersion of the determined RR intervals.
 24. The device of claim 22,wherein the predetermined approximation coefficients correspond to acoarsest scale of the plurality of scales.